XVACCR Quantitative Analyst

Crédit Agricole SA
London
6 days ago
Create job alert
Entity

About Crédit Agricole Corporate and Investment Bank (Crédit Agricole CIB) is the corporate and investment banking arm of Crédit Agricole Group, the 10th largest banking group worldwide in terms of balance sheet size (The Banker, July 2022). 8,600 employees in more than 30 countries across Europe, the Americas, Asia‑Pacific, the Middle‑East and North Africa support the Bank’s clients, meeting their financial needs throughout the world. Crédit Agricole CIB offers its large corporate and institutional clients a range of products and services in capital market activities, investment banking, structured finance, commercial banking and international trade. The Bank is a pioneer in the area of climate finance, and is currently a market leader in this segment with a complete offer for all its clients. By working every day in the interest of society, we are a Group committed to diversity and inclusion and place people at the heart of all our transformations. All our job offers are open to persons with disabilities.


Reference

2025-105227


Contract type

Permanent Contract


Job title

XVACCR Quantitative Analyst


Job summary

The candidate will join the XVACCR, Collateral & Credit Quantitative Research team—an innovative group at the forefront of quantitative modelling for XVA, Counterparty Risk, Collateral, and Credit. This dynamic team is tasked with developing cutting‑edge solutions that support a wide range of strategic and regulatory initiatives across the bank. The quant team collaborates closely with several key internal stakeholders: XVA and Scarce Resources desk for XVA pricing and modelling; Risk department for Internal & Regulatory CCR, Accounting XVA, and SIMM; Collateral desk for discounting, SIMM and IMVA with CCPs. The quant team closely works with the business to study and assess the models’ behaviour and performance. It also plays a significant role in several strategic XVA and RWA projects by producing computational blocks using cutting‑edge modelling and implementation techniques to ensure the bank can cope with the increasing list of regulatory measures (XVAVaR, SACCR, FRTB‑CVA …) and metrics needed to manage our XVA reserves properly (Optimisation modules, Sensitivities with AAD, Machine Learning …). The quant team continuously builds and upgrades XVA libraries and platforms to implement regulatory changes in an optimised architecture. The team is also actively participating in developing the Collateral management platform for CCP and EMIR Initial Margin and working on various FO and Risk systems migration projects.


Key Responsibilities

  • Define and implement mathematical tools and pricing models for XVA‑linked activity.
  • Define and implement tools and pricing models for Collateral management activity (IMVA‑CCP, SIMM …).
  • Interact and support Front Office, Risk Management and IT partners.

Special Role Requirements

  • Good knowledge of numerical methods such as Monte Carlo, optimisation algorithms, ….
  • Quantitative finance modelling skills: stochastic calculus for XVA, IR, FX, Credit, ….
  • Recent experience and strengths in distributed computing, inter‑process communication, multi‑threading programming, Microsoft Office, VC++, VBA, SQL, Access, Oracle, XML, XSLT.
  • Strong team orientation, ability to work alone and highly self‑motivated.
  • Able to adapt and learn new technologies quickly.
  • Results‑ and time‑oriented.
  • Excellent analytical and problem‑solving abilities.
  • Creative, can devise and implement multiple solutions.
  • Good communication skills – both verbal and written.

Supplementary Information

Our commitment to you – Join our team at Crédit Agricole CIB, the corporate and investment banking arm of 10th largest banking group worldwide in terms of balance sheet size (The Banker, July 2023). We offer more than just a job. You will be part of a dynamic and collaborative work environment where CSR is embraced in our day‑to‑day business operation, innovation is encouraged and diversity is celebrated. Crédit Agricole CIB, the first French bank to have committed to the Equator Principles, is a pioneer and global leader in sustainable finance. Our commitment to sustainability and corporate responsibility means that your work will have a positive impact on our communities and the environment. With a people‑centric culture where everyone is valued, and opportunities for personal and professional growth, Crédit Agricole CIB is not just a place to work – it is where you make an impact. Our hiring process is open to all and should you have any particular needs or you may require adjustments, please let us know.


Geographical area

Europe, United Kingdom


City

London


Bachelor Degree / BSc Degree or equivalent

  • Computer Science or Engineering or equivalent experience

Experience

  • Previous experience XVA and/or RWA optimisation

Required skills

  • Creativity
  • Autonomy
  • Team spirit

Technical skills required

  • Visual C++
  • SQL
  • XML, XSLT
  • Multi‑threading programming

You may be interested in these vacancies


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.